Chlorophyll fluorescence can be defined as the red and far-red light emitted by photosynthetic tissue when it is excited by a light source. This is an important phenomenon which permits investigators to obtain important information about the state of health of a photosynthetic sample. This article reviews the current state of the art knowledge regarding the design of new chlorophyll fluorescence sensing systems, providing appropriate information about processes, instrumentation and electronic devices. These types of systems and applications can be created to determine both comfort conditions and current problems within a given subject. The procedure to measure chlorophyll fluorescence is commonly split into two main parts; the first involves chlorophyll excitation, for which there are passive or active methods. The second part of the procedure is to closely measure the chlorophyll fluorescence response with specialized instrumentation systems. Such systems utilize several methods, each with different characteristics regarding to cost, resolution, ease of processing or portability. These methods for the most part include cameras, photodiodes and satellite images.

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This paper describes the development of an application for mobile devices under the iOS platform which has the objective of monitoring patients with alterations or affections from cardiac pathologies. The software tool developed for mobile devices provides a patient and a specialist doctor the ability to handle and treat disease remotely while monitoring through the technique of non-contact photoplethysmography (PPG). The mobile application works by processing red, green, and blue (RGB) color video images on a specific region of the face, thus obtaining the intensity of the pixels in the green channel. The results are then processed using mathematical algorithms and Fourier transform, moving from the time domain to the frequency domain to ensure proper interpretation and to obtain the pulses per minute (PPM). The results are favorable because a comparison of the results was made with respect to the application of a medical-grade pulse-oximeter, where an error rate of 3% was obtained, indicating the acceptable performance of our application. The present technological development provides an application tool with significant potential in the area of health.

SUMMARYInduction motors are key elements of every industrial process. A faulty motor produces interruptions on production lines, with consequences in cost, product quality, and safety. The relevance in induction motor monitoring is the ability to detect faults in incipient state. Many proposed methods consider direct connection of motors to the power supply; however, the common practice in industry is to connect them through variable speed drives (VSD), which introduce harmonics into the current supply signal that make the fault identification extremely difficult. This work proposes a statistical analysis through mean, variance, and information entropy computation, combined with sensorless rotating speed estimation for classifying different faults in induction motors using an artificial neural network. The proposed methodology examines the voltage and current signals provided by an industrial VSD that ensures a high certainty on identifying the treated faults at different rotational speed. A field programmable gate array-based implementation is developed to offer an online, system-on-chip solution for real-time condition monitoring.